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/*
* Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "kernels/StridedSlice.h"
#include "kernels/TestUtils.h"
namespace luci_interpreter
{
namespace kernels
{
namespace
{
using namespace testing;
TEST(StridedSliceTest, Float)
{
Shape input_shape{2, 3, 2};
std::vector<float> input_data{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12};
Shape begin_shape{3};
std::vector<int32_t> begin_data{0, 0, 0};
Shape end_shape{3};
std::vector<int32_t> end_data{1, 3, 2};
Shape strides_shape{3};
std::vector<int32_t> strides_data{1, 1, 1};
Tensor input_tensor{DataType::FLOAT32, input_shape, {}, ""};
Tensor begin_tensor{DataType::S32, begin_shape, {}, ""};
Tensor end_tensor{DataType::S32, end_shape, {}, ""};
Tensor strides_tensor{DataType::S32, strides_shape, {}, ""};
Tensor output_tensor = makeOutputTensor(DataType::FLOAT32);
input_tensor.writeData(input_data.data(), input_data.size() * sizeof(float));
begin_tensor.writeData(begin_data.data(), begin_data.size() * sizeof(int32_t));
end_tensor.writeData(end_data.data(), end_data.size() * sizeof(int32_t));
strides_tensor.writeData(strides_data.data(), strides_data.size() * sizeof(int32_t));
StridedSliceParams params{};
params.begin_mask = 0;
params.end_mask = 0;
params.ellipsis_mask = 0;
params.new_axis_mask = 0;
params.shrink_axis_mask = 1;
StridedSlice kernel(&input_tensor, &begin_tensor, &end_tensor, &strides_tensor, &output_tensor,
params);
kernel.configure();
kernel.execute();
std::vector<int32_t> output_shape{3, 2};
std::vector<float> output_data{1, 2, 3, 4, 5, 6};
EXPECT_THAT(extractTensorData<float>(output_tensor),
ElementsAreArray(ArrayFloatNear(output_data)));
EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(output_shape));
}
TEST(StridedSliceTest, Uint8)
{
Shape input_shape{2, 3, 2};
std::vector<float> input_data{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12};
std::vector<uint8_t> quant_input_data = quantize<uint8_t>(input_data, 1.0f, 0);
Shape begin_shape{3};
std::vector<int32_t> begin_data{0, 0, 0};
Shape end_shape{3};
std::vector<int32_t> end_data{1, 3, 2};
Shape strides_shape{3};
std::vector<int32_t> strides_data{1, 1, 1};
Tensor input_tensor{DataType::U8, input_shape, {{1.0f}, {0}}, ""};
Tensor begin_tensor{DataType::S32, begin_shape, {}, ""};
Tensor end_tensor{DataType::S32, end_shape, {}, ""};
Tensor strides_tensor{DataType::S32, strides_shape, {}, ""};
Tensor output_tensor = makeOutputTensor(DataType::U8, 1.0f, 0);
input_tensor.writeData(quant_input_data.data(), quant_input_data.size() * sizeof(uint8_t));
begin_tensor.writeData(begin_data.data(), begin_data.size() * sizeof(int32_t));
end_tensor.writeData(end_data.data(), end_data.size() * sizeof(int32_t));
strides_tensor.writeData(strides_data.data(), strides_data.size() * sizeof(int32_t));
StridedSliceParams params{};
params.begin_mask = 0;
params.end_mask = 0;
params.ellipsis_mask = 0;
params.new_axis_mask = 0;
params.shrink_axis_mask = 1;
StridedSlice kernel(&input_tensor, &begin_tensor, &end_tensor, &strides_tensor, &output_tensor,
params);
kernel.configure();
kernel.execute();
std::vector<int32_t> output_shape{3, 2};
std::vector<float> output_data{1, 2, 3, 4, 5, 6};
EXPECT_THAT(dequantize(extractTensorData<uint8_t>(output_tensor), output_tensor.scale(),
output_tensor.zero_point()),
ElementsAreArray(ArrayFloatNear(output_data)));
EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(output_shape));
}
} // namespace
} // namespace kernels
} // namespace luci_interpreter
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